The simplest way to do this is to train a neural network to map noisy instances of images to their clean counterparts, as in DnCNN [1]}. However, representative clean/noisy image pairs aren't always available, particularly in microscopy where it is difficult to reduce the noise without also changing the signal [2]}. For this reason, more flexible alternatives were developed, such as methods that only require noisy/noisy image pairs [3]} and methods that only require training sets consisting of single unpaired noisy images [4]}.
